{
  "nbformat": 4,
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  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "name": "AI_Project_Final.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2_Amu3hLJ-j5"
      },
      "source": [
        "#**Problem statement**:\n",
        "\n",
        "Predicting Gas Turbine plant's decay state coefficient, two outputs:\n",
        "**GT Compressor decay state coefficient** and **GT Turbine decay state coefficient**.\n",
        "\n",
        "Phases of the project are as follows:\n",
        "1. Data Preprocessing\n",
        "2. Exploratory Data Analysis\n",
        "3. Data Visualization\n",
        "4. Feature Selection\n",
        "5. Regression Models\n",
        "6. Comparative Study\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLAp6F0HBPMT"
      },
      "source": [
        "#Importing necessary Libraries for the project."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dfla4Phu_iPA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 16
        },
        "outputId": "9b836ff9-c376-416e-f82d-af72b80d94d6"
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import sys\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "\n",
        "#import chart_studio.plotly as py\n",
        "from plotly import __version__\n",
        "import plotly.express as px\n",
        "import cufflinks as cf\n",
        "from plotly.offline import download_plotlyjs,init_notebook_mode,plot,iplot\n",
        "init_notebook_mode(connected=True)\n",
        "#from plotly.offline import iplot, init_notebook_mode\n",
        "#import plotly.graph_objs as go\n",
        "cf.go_offline()\n",
        "\n",
        "import scipy.stats as stats\n",
        "\n",
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import statsmodels.api as sm\n",
        "from sklearn.metrics import explained_variance_score\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "#import ipyvolume as ipv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yU_R5UwUFnF7"
      },
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXqsyt-z2ZOt"
      },
      "source": [
        "#Importing Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09lnAqFuFwpG"
      },
      "source": [
        "#data = pd.read_csv('/content/drive/MyDrive/Turbine_dataset.csv')\n",
        "data = pd.read_csv('Turbine_dataset.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "VPMlACV6L-lk",
        "outputId": "785d84d9-1dca-4b1a-860d-36f9d77ab5de"
      },
      "source": [
        "data.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Lever position (lp) [ ]</th>\n",
              "      <th>Ship speed (v) [knots]</th>\n",
              "      <th>Gas Turbine shaft torque (GTT) [kN m]</th>\n",
              "      <th>Gas Turbine rate of revolutions (GTn) [rpm]</th>\n",
              "      <th>Gas Generator rate of revolutions (GGn) [rpm]</th>\n",
              "      <th>Starboard Propeller Torque (Ts) [kN]</th>\n",
              "      <th>Port Propeller Torque (Tp) [kN]</th>\n",
              "      <th>HP Turbine exit temperature (T48) [C]</th>\n",
              "      <th>GT Compressor inlet air temperature (T1) [C]</th>\n",
              "      <th>GT Compressor outlet air temperature (T2) [C]</th>\n",
              "      <th>HP Turbine exit pressure (P48) [bar]</th>\n",
              "      <th>GT Compressor inlet air pressure (P1) [bar]</th>\n",
              "      <th>GT Compressor outlet air pressure (P2) [bar]</th>\n",
              "      <th>Gas Turbine exhaust gas pressure (Pexh) [bar]</th>\n",
              "      <th>Turbine Injecton Control (TIC) [%]</th>\n",
              "      <th>Fuel flow (mf) [kg/s]</th>\n",
              "      <th>GT Compressor decay state coefficient.</th>\n",
              "      <th>GT Turbine decay state coefficient.</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>9.300</td>\n",
              "      <td>27.0</td>\n",
              "      <td>72762.205</td>\n",
              "      <td>3560.393</td>\n",
              "      <td>9753.812</td>\n",
              "      <td>644.806</td>\n",
              "      <td>644.806</td>\n",
              "      <td>1086.583</td>\n",
              "      <td>288.0</td>\n",
              "      <td>780.304</td>\n",
              "      <td>4.523</td>\n",
              "      <td>0.998</td>\n",
              "      <td>22.879</td>\n",
              "      <td>1.050</td>\n",
              "      <td>90.435</td>\n",
              "      <td>1.790</td>\n",
              "      <td>0.973</td>\n",
              "      <td>0.978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>6.175</td>\n",
              "      <td>18.0</td>\n",
              "      <td>29760.552</td>\n",
              "      <td>2306.825</td>\n",
              "      <td>8780.012</td>\n",
              "      <td>246.011</td>\n",
              "      <td>246.011</td>\n",
              "      <td>776.921</td>\n",
              "      <td>288.0</td>\n",
              "      <td>665.511</td>\n",
              "      <td>2.518</td>\n",
              "      <td>0.998</td>\n",
              "      <td>13.438</td>\n",
              "      <td>1.030</td>\n",
              "      <td>34.596</td>\n",
              "      <td>0.685</td>\n",
              "      <td>0.995</td>\n",
              "      <td>0.975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>3.144</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8375.774</td>\n",
              "      <td>1386.739</td>\n",
              "      <td>7051.621</td>\n",
              "      <td>60.318</td>\n",
              "      <td>60.318</td>\n",
              "      <td>589.764</td>\n",
              "      <td>288.0</td>\n",
              "      <td>580.587</td>\n",
              "      <td>1.392</td>\n",
              "      <td>0.998</td>\n",
              "      <td>7.566</td>\n",
              "      <td>1.020</td>\n",
              "      <td>12.477</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.980</td>\n",
              "      <td>0.978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3.144</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8377.589</td>\n",
              "      <td>1386.748</td>\n",
              "      <td>7098.469</td>\n",
              "      <td>60.339</td>\n",
              "      <td>60.339</td>\n",
              "      <td>570.651</td>\n",
              "      <td>288.0</td>\n",
              "      <td>576.565</td>\n",
              "      <td>1.390</td>\n",
              "      <td>0.998</td>\n",
              "      <td>7.409</td>\n",
              "      <td>1.021</td>\n",
              "      <td>11.678</td>\n",
              "      <td>0.231</td>\n",
              "      <td>0.984</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>6.175</td>\n",
              "      <td>18.0</td>\n",
              "      <td>29761.091</td>\n",
              "      <td>2306.825</td>\n",
              "      <td>8782.024</td>\n",
              "      <td>246.021</td>\n",
              "      <td>246.021</td>\n",
              "      <td>769.855</td>\n",
              "      <td>288.0</td>\n",
              "      <td>663.682</td>\n",
              "      <td>2.518</td>\n",
              "      <td>0.998</td>\n",
              "      <td>13.374</td>\n",
              "      <td>1.031</td>\n",
              "      <td>34.154</td>\n",
              "      <td>0.676</td>\n",
              "      <td>0.998</td>\n",
              "      <td>0.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>7.148</td>\n",
              "      <td>21.0</td>\n",
              "      <td>38990.324</td>\n",
              "      <td>2678.068</td>\n",
              "      <td>9126.509</td>\n",
              "      <td>332.301</td>\n",
              "      <td>332.301</td>\n",
              "      <td>837.934</td>\n",
              "      <td>288.0</td>\n",
              "      <td>693.706</td>\n",
              "      <td>2.972</td>\n",
              "      <td>0.998</td>\n",
              "      <td>15.649</td>\n",
              "      <td>1.035</td>\n",
              "      <td>44.862</td>\n",
              "      <td>0.888</td>\n",
              "      <td>0.967</td>\n",
              "      <td>0.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>8.206</td>\n",
              "      <td>24.0</td>\n",
              "      <td>50994.854</td>\n",
              "      <td>3087.533</td>\n",
              "      <td>9311.074</td>\n",
              "      <td>438.083</td>\n",
              "      <td>438.083</td>\n",
              "      <td>930.553</td>\n",
              "      <td>288.0</td>\n",
              "      <td>734.485</td>\n",
              "      <td>3.582</td>\n",
              "      <td>0.998</td>\n",
              "      <td>18.502</td>\n",
              "      <td>1.041</td>\n",
              "      <td>60.855</td>\n",
              "      <td>1.205</td>\n",
              "      <td>0.968</td>\n",
              "      <td>0.984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>7</td>\n",
              "      <td>6.175</td>\n",
              "      <td>18.0</td>\n",
              "      <td>29798.156</td>\n",
              "      <td>2307.217</td>\n",
              "      <td>8847.578</td>\n",
              "      <td>246.318</td>\n",
              "      <td>246.318</td>\n",
              "      <td>769.867</td>\n",
              "      <td>288.0</td>\n",
              "      <td>669.416</td>\n",
              "      <td>2.495</td>\n",
              "      <td>0.998</td>\n",
              "      <td>13.028</td>\n",
              "      <td>1.030</td>\n",
              "      <td>33.814</td>\n",
              "      <td>0.669</td>\n",
              "      <td>0.956</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>7.148</td>\n",
              "      <td>21.0</td>\n",
              "      <td>38999.258</td>\n",
              "      <td>2678.058</td>\n",
              "      <td>9121.944</td>\n",
              "      <td>332.401</td>\n",
              "      <td>332.401</td>\n",
              "      <td>816.202</td>\n",
              "      <td>288.0</td>\n",
              "      <td>686.368</td>\n",
              "      <td>2.979</td>\n",
              "      <td>0.998</td>\n",
              "      <td>15.558</td>\n",
              "      <td>1.036</td>\n",
              "      <td>43.461</td>\n",
              "      <td>0.861</td>\n",
              "      <td>0.986</td>\n",
              "      <td>0.989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>9.300</td>\n",
              "      <td>27.0</td>\n",
              "      <td>72779.442</td>\n",
              "      <td>3560.436</td>\n",
              "      <td>9789.399</td>\n",
              "      <td>644.966</td>\n",
              "      <td>644.966</td>\n",
              "      <td>1090.551</td>\n",
              "      <td>288.0</td>\n",
              "      <td>784.201</td>\n",
              "      <td>4.477</td>\n",
              "      <td>0.998</td>\n",
              "      <td>22.250</td>\n",
              "      <td>1.049</td>\n",
              "      <td>89.860</td>\n",
              "      <td>1.779</td>\n",
              "      <td>0.955</td>\n",
              "      <td>0.997</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ...  GT Turbine decay state coefficient.\n",
              "0           0  ...                                0.978\n",
              "1           1  ...                                0.975\n",
              "2           2  ...                                0.978\n",
              "3           3  ...                                1.000\n",
              "4           4  ...                                0.980\n",
              "5           5  ...                                0.980\n",
              "6           6  ...                                0.984\n",
              "7           7  ...                                1.000\n",
              "8           8  ...                                0.989\n",
              "9           9  ...                                0.997\n",
              "\n",
              "[10 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "YGWY9phvg_Uh",
        "outputId": "cb042b4f-5cb1-4c08-b757-961a28b8fe73"
      },
      "source": [
        "data.drop(columns=['Unnamed: 0'], axis = 1, inplace = True)\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Lever position (lp) [ ]</th>\n",
              "      <th>Ship speed (v) [knots]</th>\n",
              "      <th>Gas Turbine shaft torque (GTT) [kN m]</th>\n",
              "      <th>Gas Turbine rate of revolutions (GTn) [rpm]</th>\n",
              "      <th>Gas Generator rate of revolutions (GGn) [rpm]</th>\n",
              "      <th>Starboard Propeller Torque (Ts) [kN]</th>\n",
              "      <th>Port Propeller Torque (Tp) [kN]</th>\n",
              "      <th>HP Turbine exit temperature (T48) [C]</th>\n",
              "      <th>GT Compressor inlet air temperature (T1) [C]</th>\n",
              "      <th>GT Compressor outlet air temperature (T2) [C]</th>\n",
              "      <th>HP Turbine exit pressure (P48) [bar]</th>\n",
              "      <th>GT Compressor inlet air pressure (P1) [bar]</th>\n",
              "      <th>GT Compressor outlet air pressure (P2) [bar]</th>\n",
              "      <th>Gas Turbine exhaust gas pressure (Pexh) [bar]</th>\n",
              "      <th>Turbine Injecton Control (TIC) [%]</th>\n",
              "      <th>Fuel flow (mf) [kg/s]</th>\n",
              "      <th>GT Compressor decay state coefficient.</th>\n",
              "      <th>GT Turbine decay state coefficient.</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.300</td>\n",
              "      <td>27.0</td>\n",
              "      <td>72762.205</td>\n",
              "      <td>3560.393</td>\n",
              "      <td>9753.812</td>\n",
              "      <td>644.806</td>\n",
              "      <td>644.806</td>\n",
              "      <td>1086.583</td>\n",
              "      <td>288.0</td>\n",
              "      <td>780.304</td>\n",
              "      <td>4.523</td>\n",
              "      <td>0.998</td>\n",
              "      <td>22.879</td>\n",
              "      <td>1.050</td>\n",
              "      <td>90.435</td>\n",
              "      <td>1.790</td>\n",
              "      <td>0.973</td>\n",
              "      <td>0.978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6.175</td>\n",
              "      <td>18.0</td>\n",
              "      <td>29760.552</td>\n",
              "      <td>2306.825</td>\n",
              "      <td>8780.012</td>\n",
              "      <td>246.011</td>\n",
              "      <td>246.011</td>\n",
              "      <td>776.921</td>\n",
              "      <td>288.0</td>\n",
              "      <td>665.511</td>\n",
              "      <td>2.518</td>\n",
              "      <td>0.998</td>\n",
              "      <td>13.438</td>\n",
              "      <td>1.030</td>\n",
              "      <td>34.596</td>\n",
              "      <td>0.685</td>\n",
              "      <td>0.995</td>\n",
              "      <td>0.975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.144</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8375.774</td>\n",
              "      <td>1386.739</td>\n",
              "      <td>7051.621</td>\n",
              "      <td>60.318</td>\n",
              "      <td>60.318</td>\n",
              "      <td>589.764</td>\n",
              "      <td>288.0</td>\n",
              "      <td>580.587</td>\n",
              "      <td>1.392</td>\n",
              "      <td>0.998</td>\n",
              "      <td>7.566</td>\n",
              "      <td>1.020</td>\n",
              "      <td>12.477</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.980</td>\n",
              "      <td>0.978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.144</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8377.589</td>\n",
              "      <td>1386.748</td>\n",
              "      <td>7098.469</td>\n",
              "      <td>60.339</td>\n",
              "      <td>60.339</td>\n",
              "      <td>570.651</td>\n",
              "      <td>288.0</td>\n",
              "      <td>576.565</td>\n",
              "      <td>1.390</td>\n",
              "      <td>0.998</td>\n",
              "      <td>7.409</td>\n",
              "      <td>1.021</td>\n",
              "      <td>11.678</td>\n",
              "      <td>0.231</td>\n",
              "      <td>0.984</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.175</td>\n",
              "      <td>18.0</td>\n",
              "      <td>29761.091</td>\n",
              "      <td>2306.825</td>\n",
              "      <td>8782.024</td>\n",
              "      <td>246.021</td>\n",
              "      <td>246.021</td>\n",
              "      <td>769.855</td>\n",
              "      <td>288.0</td>\n",
              "      <td>663.682</td>\n",
              "      <td>2.518</td>\n",
              "      <td>0.998</td>\n",
              "      <td>13.374</td>\n",
              "      <td>1.031</td>\n",
              "      <td>34.154</td>\n",
              "      <td>0.676</td>\n",
              "      <td>0.998</td>\n",
              "      <td>0.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>2.088</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5858.780</td>\n",
              "      <td>1349.023</td>\n",
              "      <td>6736.273</td>\n",
              "      <td>23.171</td>\n",
              "      <td>23.171</td>\n",
              "      <td>581.017</td>\n",
              "      <td>288.0</td>\n",
              "      <td>564.922</td>\n",
              "      <td>1.294</td>\n",
              "      <td>0.998</td>\n",
              "      <td>6.991</td>\n",
              "      <td>1.019</td>\n",
              "      <td>21.583</td>\n",
              "      <td>0.245</td>\n",
              "      <td>0.999</td>\n",
              "      <td>0.988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>5.140</td>\n",
              "      <td>15.0</td>\n",
              "      <td>21633.743</td>\n",
              "      <td>1924.349</td>\n",
              "      <td>8497.158</td>\n",
              "      <td>175.288</td>\n",
              "      <td>175.288</td>\n",
              "      <td>696.232</td>\n",
              "      <td>288.0</td>\n",
              "      <td>635.894</td>\n",
              "      <td>2.078</td>\n",
              "      <td>0.998</td>\n",
              "      <td>10.947</td>\n",
              "      <td>1.026</td>\n",
              "      <td>24.481</td>\n",
              "      <td>0.485</td>\n",
              "      <td>0.971</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>8.206</td>\n",
              "      <td>24.0</td>\n",
              "      <td>50994.819</td>\n",
              "      <td>3087.555</td>\n",
              "      <td>9324.455</td>\n",
              "      <td>438.051</td>\n",
              "      <td>438.051</td>\n",
              "      <td>928.531</td>\n",
              "      <td>288.0</td>\n",
              "      <td>737.443</td>\n",
              "      <td>3.560</td>\n",
              "      <td>0.998</td>\n",
              "      <td>18.200</td>\n",
              "      <td>1.041</td>\n",
              "      <td>60.306</td>\n",
              "      <td>1.194</td>\n",
              "      <td>0.953</td>\n",
              "      <td>0.996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>6.175</td>\n",
              "      <td>18.0</td>\n",
              "      <td>29761.078</td>\n",
              "      <td>2306.850</td>\n",
              "      <td>8793.302</td>\n",
              "      <td>245.973</td>\n",
              "      <td>245.973</td>\n",
              "      <td>783.490</td>\n",
              "      <td>288.0</td>\n",
              "      <td>668.558</td>\n",
              "      <td>2.513</td>\n",
              "      <td>0.998</td>\n",
              "      <td>13.412</td>\n",
              "      <td>1.030</td>\n",
              "      <td>34.919</td>\n",
              "      <td>0.691</td>\n",
              "      <td>0.982</td>\n",
              "      <td>0.975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>8.206</td>\n",
              "      <td>24.0</td>\n",
              "      <td>50990.504</td>\n",
              "      <td>3087.329</td>\n",
              "      <td>9300.796</td>\n",
              "      <td>438.031</td>\n",
              "      <td>438.031</td>\n",
              "      <td>929.296</td>\n",
              "      <td>288.0</td>\n",
              "      <td>731.610</td>\n",
              "      <td>3.597</td>\n",
              "      <td>0.998</td>\n",
              "      <td>18.694</td>\n",
              "      <td>1.041</td>\n",
              "      <td>61.034</td>\n",
              "      <td>1.208</td>\n",
              "      <td>0.981</td>\n",
              "      <td>0.977</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Lever position (lp) [ ]  ...  GT Turbine decay state coefficient.\n",
              "0                       9.300  ...                                0.978\n",
              "1                       6.175  ...                                0.975\n",
              "2                       3.144  ...                                0.978\n",
              "3                       3.144  ...                                1.000\n",
              "4                       6.175  ...                                0.980\n",
              "...                       ...  ...                                  ...\n",
              "9995                    2.088  ...                                0.988\n",
              "9996                    5.140  ...                                1.000\n",
              "9997                    8.206  ...                                0.996\n",
              "9998                    6.175  ...                                0.975\n",
              "9999                    8.206  ...                                0.977\n",
              "\n",
              "[10000 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6LmxD7aUw2zy"
      },
      "source": [
        "data.rename(columns={'Lever position (lp) [ ]':'Lever_position',\n",
        "                     'Ship speed (v) [knots]':'Ship_speed',\n",
        "                     'Gas Turbine shaft torque (GTT) [kN m]':'Gas_turbine_shaft_torque',\n",
        "                     'Gas Turbine rate of revolutions (GTn) [rpm]':'Gas_turbine_rate_of_revolutions',\n",
        "                     'Gas Generator rate of revolutions (GGn) [rpm]':'Gas_generator_rate_of_revolutions',\n",
        "                     'Starboard Propeller Torque (Ts) [kN]':'Starboard_propeller_torque',\n",
        "                     'Port Propeller Torque (Tp) [kN]':'Port_propeller_torque',\n",
        "                     'HP Turbine exit temperature (T48) [C]':'HP_turbine_exit_temperature',\n",
        "                     'GT Compressor inlet air temperature (T1) [C]':'GT_compressor_inlet_air_temperature',\n",
        "                     'GT Compressor outlet air temperature (T2) [C]':'GT_compressor_outlet_air_temperature',\n",
        "                     'HP Turbine exit pressure (P48) [bar]':'HP_turbine_exit_pressure',\n",
        "                     'GT Compressor inlet air pressure (P1) [bar]':'GT_compressor_inlet_air_pressure',\n",
        "                     'GT Compressor outlet air pressure (P2) [bar]':'GT_compressor_outlet_air_pressure',\n",
        "                     'Gas Turbine exhaust gas pressure (Pexh) [bar]':'Gas_turbine_exhaust_gas_pressure',\n",
        "                     'Turbine Injecton Control (TIC) [%]':'Turbine_injection_control',\n",
        "                     'Fuel flow (mf) [kg/s]':'Fuel_flow',\n",
        "                     'GT Compressor decay state coefficient.':'GT_compressor_decay_state_coefficient',\n",
        "                     'GT Turbine decay state coefficient.':'GT_turbine_decay_state_coefficient'}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DxQRJvwlw6UN",
        "outputId": "fce1f598-3058-48f5-8d54-c95628585c99"
      },
      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Lever_position', 'Ship_speed', 'Gas_turbine_shaft_torque',\n",
              "       'Gas_turbine_rate_of_revolutions', 'Gas_generator_rate_of_revolutions',\n",
              "       'Starboard_propeller_torque', 'Port_propeller_torque',\n",
              "       'HP_turbine_exit_temperature', 'GT_compressor_inlet_air_temperature',\n",
              "       'GT_compressor_outlet_air_temperature', 'HP_turbine_exit_pressure',\n",
              "       'GT_compressor_inlet_air_pressure', 'GT_compressor_outlet_air_pressure',\n",
              "       'Gas_turbine_exhaust_gas_pressure', 'Turbine_injection_control',\n",
              "       'Fuel_flow', 'GT_compressor_decay_state_coefficient',\n",
              "       'GT_turbine_decay_state_coefficient'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vcuxVnr_w9cS",
        "outputId": "c1389237-7df6-4e7a-acde-5731d080084c"
      },
      "source": [
        "data.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lever_position                           0\n",
              "Ship_speed                               0\n",
              "Gas_turbine_shaft_torque                 0\n",
              "Gas_turbine_rate_of_revolutions          0\n",
              "Gas_generator_rate_of_revolutions        0\n",
              "Starboard_propeller_torque               0\n",
              "Port_propeller_torque                    0\n",
              "HP_turbine_exit_temperature              0\n",
              "GT_compressor_inlet_air_temperature      0\n",
              "GT_compressor_outlet_air_temperature     0\n",
              "HP_turbine_exit_pressure                 0\n",
              "GT_compressor_inlet_air_pressure         0\n",
              "GT_compressor_outlet_air_pressure        0\n",
              "Gas_turbine_exhaust_gas_pressure         0\n",
              "Turbine_injection_control                0\n",
              "Fuel_flow                                0\n",
              "GT_compressor_decay_state_coefficient    0\n",
              "GT_turbine_decay_state_coefficient       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-LYXx_ST2P2g"
      },
      "source": [
        "#Data Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64bliukdpE0F",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "42a5e04a-c9d4-4ef2-9688-88a2989dc59e"
      },
      "source": [
        "sns.lineplot(x = 'Ship_speed', y = 'GT_compressor_decay_state_coefficient', data = data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f76adf7aac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y01wlm61pE2q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "34eb7691-acfb-498c-b064-9ba4924de049"
      },
      "source": [
        "sns.distplot(data.GT_turbine_decay_state_coefficient)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7694c8f1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "GO4OZIuzDr-M",
        "outputId": "c2701ab1-8588-4eb2-cf14-1f4801e0e76b"
      },
      "source": [
        "sns.lineplot(x = 'Lever_position', y = 'GT_turbine_decay_state_coefficient', data = data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7693c5e978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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i7xORdSKyXkS+OMz2mSLymIi8LCKPi0h71rZbRGS1iKwVke+Lp15EXsp67BOR7/r7zxCRpSKy0j/fhXl8PmOMMUcpn3EqbwJni0id/7pvtGNEJAD8CLgA2Aa8ICJLVHVN1m63Anep6p0icj5wM/AxEVkEnAtkuiw/DSxW1ceBBVnvsQJ4wH95E3Cfqv67iMzHG6A5K9fPaIwx5ujkMk7lo6r6CxG5Ych6AFT1thEOPxNYr6ob/GPuAS4CspPKfCBz7qXAb/xlBaqAMN78aSFg95AY5gITgaeyjmnwlxuBHaN9PmOMMYWTS/VXZl7k+sM8RjIN2Jr1epu/Ltsq4FJ/+RKgXkRaVfVZvCSz0388oqprhxx7BXCvqmYGZX4V+KiIbMMrpXx6uKBE5BoRWS4iy/fu3TvKRzDGGJOrXKq/jvOf16jqr4oQw+eAH4rIJ4Ange1AWkSOB+YBmTaWR0XkHar6VNaxVwAfy3p9JfAzVf2OiJwD/FxETvanmBmkqrcDtwMsXLhwXM+0bIwxhZRLSeVC8eq6bjyC828Hpme9bvfXDVLVHap6qaqeDvyjv64Lr9SyTFX7/Pabh4BzMseJyGl43ZxXZJ3uKuA+/xzP4lWftR1B3MYYY45ALknlYaATOFVEerIevSLSM8qxLwAniMhsf0zLFcCS7B1EpE1EMnHcCNzhL28BFotIUERCwGIgu/rrSuDuIe+3Bf9OlCIyDy+pWP2WMcaMkVySyk2q2gT8XlUbsh71qtow0oGqmgKuAx7BSwj3qepqEfl61piX84B1IvI63rQv3/TX3w+8iTfV/ipglapm37/lcg5NKp8FrhaRVf62T2S1txhjjCmyXNpUngXOAEYrlQxLVR/EazTPXvflrOX78RLI0OPSjDAORlXnDLNuDV43ZGOMMSWQS1IJi8iHgUUicunQjar6wDDHGGOMGYdySSp/A3wEaAL+fMg25cDAQ2OMMeNcLlPfPw08LSLLVfUnYxCTMcaYCpXP3F/3iMhNInI7gIicICJ24y5jjDGD8kkqdwAJDtxTZTvwjYJHZIwxpmLlk1SOU9VbgCSAqg7gzclljDHGAPkllYR/C2EFEJHjgHhRojLGGFOR8rnz41fwRtdPF5Ff4o0H+UQxgjLGGFOZ8rmfyqMi8iJwNl611/Wqand+NMYYMyifkgp4pZN3Zr3+3wLGYowxpsLl3KYiIt8Grse7wdYa4HoR+VaxAjPGGFN58impXAgsyNybRETuBFYCXypGYMYYYypPPr2/wJuqJaOxkIEYY4ypfPmUVG4GVorIUryG+ncCXyxKVMYYYypSPr2/7haRx4G3+av+QVV3FSUqY4wxFSmfhvpLgAFVXaKqS4CYiFxcvNCMMcZUmnzaVL6iqt2ZF/595L9S+JCMMcZUqnySynD75jvOxRhjzDEsn6SyXERuE5Hj/MdtwIpiBWaMMaby5JNUPo039f29wD1ADLi2GEEZY4ypTPn0/upnhC7EIvIDVf10QaIyxhhTkfId/DiScwt4LmOMMRWokEnFGGPMOGdJxRhjTMEUMqnYrYWNMWacyzupiEjNYTZ97yhjMcYYU+HymaZlkYisAV7zX58mIv+W2a6qPyt8eMYYYypJPiWVfwXeC+wHUNVVHHwXSGOMMeNcXtVfqrp1yKp0AWMxxhhT4fKZu2uriCwCVERCeLcWXlucsIwxxlSifEoqf4M3Lcs0YDuwAPjb0Q4SkfeJyDoRWS8ih4zIF5GZIvKYiLwsIo+LSHvWtltEZLWIrBWR74unXkReynrsE5HvZh1zuYis8Y/77zw+nzHGmKOUT0nlRFX9SPYKETkXeOZwB4hIAPgRcAGwDXhBRJao6pqs3W4F7lLVO0XkfLw7TH7MLxWdC5zq7/c0sFhVH8dLaJn3WAE84C+fANwInKuqnSIyMY/PZ4wx5ijlU1L5QY7rsp0JrFfVDaqawJuI8qIh+8wH/uQvL83arkAVEAYiQAjYnX2giMwFJgJP+auuBn6kqp0AqrpnlPiMMcYU0KglFRE5B1gETBCRG7I2NQCBUQ6fBmQ37m8DzhqyzyrgUrxxLpcA9SLSqqrPishSYCfewMofqurQNpwrgHtVVf3Xc/2Yn/Fj+6qqPjzMZ7oGuAZgxowZo3wEY4wxucqlpBIG6vASUH3Wowf4iwLE8DlgsYisBBbjtdekReR4YB7QjpeczheRdww59grg7qzXQeAE4DzgSuC/RKRp6Buq6u2qulBVF06YMKEAH8EYYwzkUFJR1SeAJ0TkZ6q6Oc/zbwemZ71u99dln38HXkkFEakDLlPVLhG5Glimqn3+toeAc/CrukTkNCCoqtk3CtsGPKeqSWCjiLyOl2ReyDNuY4wxRyCfNpUBEfkXEXlQRP6UeYxyzAvACSIyW0TCeCWLJdk7iEibiGTiuBG4w1/egleCCfpdmBdzcBfmKzm4lALwG7xSCiLShlcdtiGPz1iR0q7y1Bt76Y4mSx2KMWacyyep/BJvipbZwNeATYxSAlDVFHAd8AheQrhPVVeLyNdF5EP+bucB6/xSxSTgm/76+4E3gVfw2l1Wqervsk5/OYcmlUeA/f50MkuBz6vq/jw+Y8Xpi6X42u9Wc8sj67ju7hdZvqmj1CEZY8YxOdDGPcqOIitU9a0i8rKqnuqve0FV31bUCIts4cKFunz58iM69qWtnSRTSlVotP4KxbG1Y4B/+v0a9vbGufLMGTz1xl427R/g/SdP5q/PnV2yuIwx5a9rIMGstlqmtxxujuCR+Tlh4dD1+YxTydSt7BSRDwA7gJYjisYctec37ufWP7xOJOTwrUtOYd6UBi5eMI2fL9vMb17azsvburnhgrnMnVRf6lCNMeNIPtVf3xCRRuCzeD22fgx8pihRmcNSVe5bvpVv/H4t05qque0vFzBvSgMA4aDDVW+fzTcuPpl4Ks0Xfv0y976whbSbW2nUGGOOVj4llU5V7Qa6gXfB4Ih6M0ZiyTTfe+wNnl6/j8VzJ/Dp848nEjy0iuu09iZ+cMUZ/PsTb/KL57awfHMnN1wwlymN1SWI2hgznhR7RL0pkD09Mf7h1y/zzPp9fHLRLD57wdxhE0pGXVWQz7/3RD57wVy2dgxw/T0v8eiaXeTahmaMMUei2CPqTQG8ur2bbz/8Gqm0y5f/fD4LZ+belHXeiROZP7WB7/7xDb7/p/U8v6mD6951Ao3VoSJGbIwZr8phRL0ZwUOv7uSm375KXSTIrX95Wl4JJWNifRXfuPhk/vrcWSzf1Ol1Pd5sXY+NMYV3RCPq/cGKdaraU+wAx6tk2uX2Jzfw8OpdLJzZzOfecyK1kXyawA7miHDJ6e0smN7Md/6wjq/9bg0XnjKFTy6aZV2PjTEFk0+bys0i0iAitcCrwBoR+XyR4hrXugYS/H+/fZWHV+/iL85o56YPzD+qhJJtdlstt12+gIsXTOXBV3bymXtf4o3dvQU5tzHG5JNU5vslk4uBh/BG1n+sKFGNY2/u7eOGX63ijd19fO49J/LxRbMIOFLQ9/C6Hs/hGxedTCyZ5vO/fpl7l2+1rsfGmKOWT1IJ+XNwXQws8SdttG+hAnrqjb184dcvo6r882WnsnhucWdQPm16Ez+88gwWHdfKL5Zt5sYHXmZXd6yo72mMObblk1T+E2++r1rgSRGZiddYb46Sq8pdz27ilkfWcdyEOm67fAHHT6wbk/euqwry+fd4XY+3dAzwd/es5I9rdlvXY1PW+uMpXtvZQzLtljoUM0TOFfWq+n3g+5nXIrIFfxCk//rjqnpnYcM79g0kUnznD6/z/KYO3jt/Ep9afByhQD65/uiJyGDX43999HW+96c3eH5TB9e+63jremzKyv6+OEtW7eChV3cRTaapCQdYOLOZs+e08taZzdSEC9P2aI5czhNKjnoikRdV9YyCnGwMlXJCyR1dUb7x+zVs74pyzTvmcOEpUxApbPtJvtKu8tuXtvPzZZuprwryd+8+4Yi6MRtTSFs7BvifldtZum4PrirnHt/GmbNaeHl7N89v7KA7miToCKe2N3H2nBbOmt1KS2241GGXtWJNKFnIpLJSVU8vyMnGUKmSyotbOrnlkddwRLjxfSdxSvshN6gsqY37+rj1D6+zpWOAD5wyhU9Y12NTAmt29vDAi9t4bmMH4aDDBfMmcfGCaUxurBrcJ+0qr+3qYdmGDp7buJ+dfrvgiZPqOWtOC2fPaWV685F9cR7LKiGpWEklB6rKb1/awU//byMzWmq46QPzmdRQNfqBJZBIudz57CaWrNpBe3M1n73gxDFr6zHjl6vKC5s6+PWKbazd1Ut9JMgHT53CB06dOmp1rKqypWOAZRs7WLZhP+v39AEwramas+e0cPbsVuZOrscpcY1AOaiEpGIllVEkUi4/XPoGS9ftZdFxrXzm3XOpDpf/1f9LW7v47h9fpyua5MNnzuCyM9oL3s3ZmGTa5fF1e3hg5Xa2dUaZWB/hktOn8WfzJh1xKXlvb5znN+5n2cYOXtneTdpVmmtCnDm7lbPntHBae9OYt2GWi5InFREJqGp6hO0/VNXrjii6EhqrpLK/L843H1zLG3v6+MhZM7h84fSKulrqjSX5t8ff5On1+5g3pYEbLpjL5DItYZnK0h9P8fDqXSx5aQcdAwnmtNVy6RntvP34toJevPTFUyzf1MGyjR28uLmTaDJNdSjAGTObOXt2CwtntVBXoEHGlaAcksoG4NfAT1V1zRFFUYbGIqm8tquHmx98jWgyzQ0XzOXsOa1H9H6lpqo8/vpe/uOJN1GFa945h3efNLHknQtMZRrak2vB9CYuPX0aC6Y3Ff1vKpFyeXl712A7TNdAkoAjnDKtkbPntHLW7Bba6iJFjaHUyiGp1ANXAJ/EG99yB3BPpc//Veyk8se1u/nR0vW01UW46QPzmNlae6Shlo09PTH+9Y+v8+qOHs6Z02pdj01ehvbkevvxbVxyenvJ2utcVV7f1cuyjftZtqGD7V1RAI6fWMfZc1o5e3YLM1pqjrmLp5InlSEnWwz8N9AE3A/8k6quP6LISqxYSSXtKnc8s5Elq3awYHoTX3jvidRXHTtfvGlX+c1L2/mF3/X4M++eyxkzm0sdliljufTkKgdbOwdYtmE/z23oYJ0/L96UxqrBEsxJkxuOiTbFkicVEQkAH8ArqcwCfg78EngH8C1VnXtEkZVYMZJKTzTJLY+8xqpt3XzotKn89bmzj4k/wuFs2NvHdx71uh5/8JQpfNy6HpssrirPb+zggRf9nlxVQT54Sm49ucrB/r44z2/yepK9vK2blKs0Voc4c7bXk+y06Y0j3iyvnJVDUtkALAV+oqr/N2Tb91X1744oshIrdFLZvL+fb/x+Lfv64lx73vH82fxJhQq1bMVTae56drN1PTaDitGTq9T64yle3NLJsg37eWGT19BfFXI4Y0YzZ81u5W2zmiuqNqIckkqdqvYd0buXsUImlWUb9nPbo69TFXL40oXzOGlyQyFDLXsrt3Ty3cfeoDua5CNnzuBS63o87hzSk2tCLZed3s65Be7JVWrJtMsr27pZttGrJusYSOAInDytkbNnt3LWHK+hP+3qwQ9V3Kzl4banXX8fJWube9Brd7jj9TDvc5jtsUSaq94+mwveMvmIfgaFSCpVwFXAW4DBSlBV/esjiqhMFCKphIMO9y3fyi+f28IJE+v4xwvn0XqM9xw5nN5Ykh89/ibPrN/H/CkN/H2ZdT12VUmkXBIpl2TapTYSrNgr53IyXE+uy85o57T2xmOugXsoV5X1e/pYtmE/yzbsZ2tntNQhARB0BMcRAiIEnCEP/3fyD+87kUvOaD+i8xciqfwKeA34MPB14CPAWlW9/ogiKhNHm1R6oin+44k3+b839/OuEydw3btOIBwcn4OpMlSVpev28p9Pel2PP/XOOZw/pOtx5ss9mfa+4BP+c9xfF8/elrX9sMuHex6ynBpyzxgBpjZVM6u1hllttcxqrWVWWy0T6yMVNY6oVLZ2DPDAym08vm5vWfTkKgfbO6Ms39zBQCJ90Je4k7UccCDgOFnbIZCVBAYTwpCkkL09MELSyOVvtxyqv1aq6uki8rKqnurfW+UpVT37iCIqE0eTVB5ZvZNv/d0m5nEAAB2uSURBVP41tnYO8MlFs7lowdRj/qosH3t6Ytz2x9dZvaOH1towaVWSfuIY+uWer3DAIRQUIoEA4aBDKOgQCfjPQYdQQAgHA4QP9xwUQgGHroEkG/f1s2l//+CcUQDVocAhiWZWa43Ngusb2pPrPfMmcdHp08qqVGpGVqykks9/SDITi4icDOwCJh5RNMeA/3tzH5/71cu4rvKVD77FutMOY2JDFd+8+BR+/8pO1u/pPeyXe9hPDOGgQzjgHLo85DkUkKIk72gizeaOfjbtG2DTfi/RPPn6Xh5K7DrwmeojzG7zkszsVi/hTG6sOqbaCw5nuJ5cHz5zBheeMqUienKZsZFPUrldRJqBm4AlQB3w5aJEVQGe29BBY3WIL7z3RGa3jd+i/mgCjvCh06aWOoycVIcDnDS54aAOFqrK3r74QYlm075+XtjUQaawFQ46zGw5UKqZ3VrDzNZaGirsi1ZViSVdemJJeqJJemKpweXuaJJnN+wf7Mn1N++cw7sruCeXKZ6CTShZqY60+st1lec27ifoOPaPNQ7FU2m2dkTZtL9/sPps475+emOpwX1aa8MHqs9aa5jdVsu0pmqCYzSBYSyZpjcrMfTEUv7zkGV/W28sSTI9/PeBIzCnrY5LTp92zPXkGq9KXv0lIt8CblHVLv91M/BZVb1plOPeB3wPCAA/VtVvD9k+E2/KlwlAB/BRVd3mb7sFb8ClAzwKXI9XQnoq6xTtwC9U9TNZ57wMb6T/21T1yBpMRuE4QnU4QDI1vpPyeBUJBjh+Yt1BDdKqSudAkk2ZJOOXalZt7RpsQwo6wvSWmsEkk2mvaa4Z+YZSybR7SOnh4MTgr89aTqSGv9Wu4N1GuqEqREN1iEkNVZwwMURDtb+uKmu52ntdEwlYxwWTk3yqv96vql/KvFDVThG5EK86bFj+KPwfARcA24AXRGTJkAkpbwXuUtU7ReR84GbgYyKyCDgXONXf72lgsao+DizIeo8VwANZr+vxks9zeXw2Y46aiNBSG6alNnxQG1sy7bK9MzpYfbZx3wCrtnWzdN3ewX2aqkPMaqtlSmMV0WT6QJKIJumNpYgmDztBOLWRwGAyaKkJM6u1dpjEEBxMEHWRoJU0TNHkk1QCIhJR1TiAiFQDow3GOBNYr6ob/GPuAS4CspPKfOAGf3kp8Bt/WfHGw4TxLq5CwO7sk4vIXLzOAtkll38C/hn4fB6fzZiiCQUcrxqs7eDJRLujycE2Gu95gKf27KM2EqDeTxLtzdUHJ4YhSaI+Ehyz6jRjcpFPUvkl8JiI/NR//UngzlGOmQZszXq9DThryD6rgEvxqsguAepFpFVVnxWRpcBOvKTyQ1VdO+TYK4B71W8YEpEzgOmq+nsROWxSEZFrgGsAZsyYMcpHMKY4GqtDnNbexGllditpY45Gzpc4qvrPwDeAef7jn1T1lgLE8DlgsYisBBYD24G0iBzvv087XnI6X0TeMeTYK4C7AUTEAW4DPpvDZ7ldVReq6sIJEyYU4CMYY4yB/EoqAGuBlKr+UURqRKReVXtH2H87MD3rdbu/bpCq7sArqSAidcBlqtolIlcDyzLzjYnIQ8A5+FVdInIaEFTVFf6p6oGTgcf9MQyTgSUi8qFiNdYbY4w5WM4lFf9L/n7gP/1V0zjQ/nE4LwAniMhsEQnjlSyWDDlvm1/KALgRrycYwBa8EkzQH72/GC+pZVyJX0oBUNVuVW1T1VmqOgtYBlhCMcaYMZRPC9+1eL2xegBU9Q1GGVGvqingOuARvIRwn6quFpGvi8iH/N3OA9aJyOvAJOCb/vr7gTeBV/DaXVap6u+yTn85WUnFGGNM6eVT/RVX1URmegwRCeL10BqRqj4IPDhk3Zezlu/HSyBDj0sDnxrhvHNGed/zRovNGGNMYeVTUnlCRL4EVIvIBcCvgN+NcowxxphxJJ+k8kVgL1511KfwSh8jjqY3xhgzvuRc/aWqLvBf/sMYY4w5xKhJRUReYYS2E1U99XDbjDHGjC+5lFQ+6D9f6z//3H/+KDk01BtjjBk/Rk0qqroZQEQuUNXTszb9g4i8iNfWYowxxuTVUC8icm7Wi0V5Hm+MMeYYl884lauAO0Sk0X/dBfx14UMyxhhTqfLp/bUCOC2TVFS1O3u7iHxcVUebtdgYY8wxLO/qK3+Ore5hNl1fgHiMMcZUsEK2idit5IwxZpwrZFKx7sXGGDPOWUnFGGNMwRQyqTxTwHMZY4ypQPncpGuSiPzEvwMjIjJfRK7KbFfV64oRoDHGmMqRT0nlZ3g325rqv34d+EyhAzLGGFO58kkqbap6H+DC4F0d00WJyhhjTEXKJ6n0i0grfi8vETkbGG68ijHGmHEqn2labgCWAMeJyDPABOAvihKVMcaYipTPNC0vishi4ES87sPrVDVZtMiMMcZUnHxKKgBnArP8484QEVT1roJHZYwxpiLlnFRE5OfAccBLHGigV8CSijHGGCC/kspCYL6q2nQsxhhjhpVP769XgcnFCsQYY0zly6ek0gasEZHngXhmpap+qOBRGWOMqUj5JJWvFisIY4wxx4Z8uhQ/UcxAjDHGVL5Rk4qIPK2qbxeRXg6+Z4oAqqoNRYvOGGNMRRk1qajq2/3n+uKHY4wxppLldT8VETlDRP5ORD4tIqfneMz7RGSdiKwXkS8Os32miDwmIi+LyOMi0p617RYRWS0ia0Xk++KpF5GXsh77ROS7/v43iMga/1yPicjMfD6fMcaYo5PP/VS+DNwJtOL1BPuZiNw0yjEB4EfA+4H5wJUiMn/IbrcCd6nqqcDXgZv9YxcB5wKnAicDbwMWq2qvqi7IPIDNwAP+uVYCC/1z3Q/ckuvnM8YYc/TyKal8BHibqn5FVb8CnA18bJRjzgTWq+oGVU0A9wAXDdlnPvAnf3lp1nYFqoAwEAFCwO7sA0VkLjAReApAVZeq6oC/eRnQjjHGmDGTT1LZgfclnxEBto9yzDRga9brbf66bKuAS/3lS4B6EWlV1WfxksxO//GIqq4dcuwVwL2HGeV/FfDQcEGJyDUislxElu/du3eUj2CMMSZXufT++gFeqaEbWC0ij/qvLwCeL0AMnwN+KCKfAJ7ES1RpETkemMeB0sajIvIOVX0q69grGKa0JCIfxZtWZvFwb6iqtwO3AyxcuNCmnTHGmALJZZzKcv95BfA/Wesfz+HY7cD0rNftDCndqOoO/JKKiNQBl6lql4hcDSxT1T5/20PAOfhVXSJyGhBU1RXZ5xORPwP+Ea/9JY4xxpgxk0uX4jv9Bve7VPUjeZ7/BeAEEZmNl0yuAD6cvYOItAEdquoCNwJ3+Ju2AFeLyM14Y2IWA9/NOvRK4O4h5zod+E/gfaq6J89YjTHGHKWc2lRUNQ3MFJFwPif372N/HfAIsBa4T1VXi8jXRSQzZ9h5wDoReR2YBHzTX38/8CbwCl67yypV/V3W6S9nSFIB/gWoA37ldzdekk+8xhhjjk4+c39tAJ7xv6j7MytV9baRDlLVB4EHh6z7ctby/XgJZOhxaeBTI5x3zjDr/mykWIwxxhRXPknlTf/hADa63hhjzCHymVDya8UMxBhjTOXL53bCSzl4QkkAVPX8gkZkjDGmYuVT/fW5rOUq4DIgVdhwjDHGVLJ8qr9WDFn1jH8XSGOMMQbIr/qrJeulA7wVaCx4RMYYYypWPtVfK/DaVASv2msj3vxapgwkUi7JtEttJJ9fqTHGFFY+30DzVDWWvUJEIgWOxxyBaCJNNJmivirIvv4YzdURAo6UOixjzDiUzyzF/zfMumcLFYg5Mn2xFAk3zRkzm1kwvZkTJtbTFU3QF7M+FMaYsZfLLMWT8aarr/bn1spcAjcANUWMzYyiO5ogHHRYMK2F6nAAgPbmGlpqw6zb1WulFmPMmMul+uu9wCfwZhj+DgeSSg/wpeKEZUbT0R+nsSbM/CkNhIMHFzhrwkFOa29iR3eU9Xv6iAQC1FVZW4sxpvhymqUYuFNELlPVXx9uPxH5uL+vKSJVZX9/gkkNEeZOqicYGL4G03HESi3GmDGXc5vKSAnFd/1RxmJGkXaVff1xpjdXc9LkhsMmlGyZUou1tRhjxkI+DfWjsUvgIkqlXfYPxDlhYj3HTazDyaPEkSm1nDm7haqww77+GGnXbnhpjCm8QiYV+5YqkkTKpSua5OQpjUxvqUHkyPJ3ptQyd2KDlVqMMUVhJZUyF02k6YsnOX1GE5Maq476fI4jTGuutlKLMaYoRk0qInJpjud65ihjMUNkj0FpqsnrppujslKLqXSptIurdkFUbnIpqdyUy4lU9bqjjMVk6Y4mCATgrTNaqK8KFeU9rNRiKlEq7bK/P05/IkXXQIL9/XFiyXSpwzI+G7xQhkYag1IMmVLLzu4Y6/f2EXYcG9diyk7aVbqiCQKOcPzEOiY3VOEqdPbH2dIxwL6+GKFAgLpI0LrOl1Au3xwnicjLw6wXQFX11ALHNG7lOgalGDKllubaEK/v7rNxLaZsuKr0xJKkXWVWay1Tm6oPutia1FjNxIYqeuMpdnfH2NEdxXWhLhKkKhQoYeTjUy5JZSPw58UOZLxLu0rHQJwZzTXMmZBfl+FCqgkHOXVao5VaTMmpKj2xFCnXZVpTNdNbag6bJESEhqoQDVUhZrXV0tmf8EsvcUIBx0ovYyiXb4u4qm4ueiTjWCrt0hFNMHdiPe3N1UfcZbhQhpZa9vbFaK4Oj2nJyYxvfbEUsVSayY1VzGytoSac+4VNKOAwsaHKK73EkuzuibGz22svrAkFB+fJM8WRy28qLCLXquqPAETkOWCCv+0Lqnp/0aIbBxIpl56YNwalEF2GC8lKLWasDSRS9CdStNVFOLmt4ag7qdRXhaivCjGrtZaO/gRbO6Ps64sTdIT6qpCVXoogl2+IHmBJ1usI8DagFvgpYEnlCGXug3L6jKaCdxkuFCu1mLEQS6bpi6eorw7y1hktNNYUtsdjMKv00h9Psas7xs7uKClXqQ4F8ioJmZHl8pMMqerWrNdPq+p+YL+I1BYprmNeXyxFGpczZjYXrctwIWVKLbu6Y7y+p5dwwKmIuE15S6RceuIJakJBTm1vpKU2XPTq39pIkOMm1jGztYauaJKtfs+xoONY6aUAckkqzdkvhoxHmYDJ23D3QakEjiNMba6myUot5iil0t7UQ5GQw1umNNJWFxnzzinBgENbXYS2uggDiRS7e2Js77TSy9HK5af2nIhcrar/lb1SRD4FPF+csI5dHf1xGqpDvGVq45iMQSkGK7WYI5V2le5YgoAIJ06qZ1JjVVmUDGrCQWa31TGjpZaugQTbOr2eYwFHqI8E7cIpD7kklb8HfiMiHwZe9Ne9Fa9t5eJiBXasKeUYlGKwUovJh6tKTzSJq8rstlqmNFUTKsO/lYAjtNZFaK2LEE2k2dMTY2tnlJSbpCoYoCYcKHnvzHKXy0269gCLROR84C3+6t+r6p+KGtkxpFzGoBSDlVrMSDJjTZJpl+ktNUxvqSYSrIwq3+pwgJlttUxv8dpetnUO0NGfQPB6lZVjUiwHOVca+kkk70QiIu8DvgcEgB+r6reHbJ8J3IHXPtMBfFRVt/nbbgE+gDdH2aN4NwKrA57KOkU78AtV/YyIRIC78EpS+4G/UtVN+cZcSOU2BqUYrNRihtMbSxJPpZnSVM3MltqKaj/M5jhCS22YltowsaRXetnWFaUnliQSDFBrpZeDFLUlSkQCwI+AC4BtwAsiskRV12Ttditwl6re6ZeGbgY+JiKLgHOBzDQwTwOLVfVxYEHWe6wAHvBfXgV0qurxInIF8M/AXxXtA44ikXLpjiXKcgxKMVipxYA31mQgkWJCfYRZbU3URY6dBu+qUIAZrbW0N9fQHU2yvSvK3t44ItBgpReg+BNKngmsV9UNACJyD3ARkJ1U5gM3+MtLgd/4ywpUAWG8ecZCwO7sk4vIXGAiB0ouFwFf9ZfvB34oIqI69vNjZ8agnDGj8NPWlzMrtRydtKuIgFOBV76xZJr+RJLG6jAnTWmhsfrYvaBwHKG5NkyzX3rZ2xtnW+cAPbEEkUCQ2sj4Lb0UO6lMA7LHuGwDzhqyzyrgUrwqskuAehFpVdVnRWQpsBMvqfxQVdcOOfYK4N6spDH4fqqaEpFuoBXYV8DPNKpKG4NSDENLLUHHIRJ0CDiCI4IjjIt/urSruKqk0kpa1XvtKi7qzcgKiEDmLzgUcEi7Lq5m7nonKIogBBwhFPCeg45TFr2m4MCsEHWRAKe2N9NcExoXv9uMqlCA6S01TGuqpieWZEdXjN29MVCv4T/kOAQDQtCRcXFxVQ7l0s/hlSg+ATwJbAfSInI8MA+vzQTgURF5h6pmt6dcAXws3zcUkWuAawBmzJhxFKEfqlLHoBRDdqllS8cAA4k0qZRL0lVSaXfw/tPif70qDH7RZtY7/lW742Qt+0nJ8RPUWDqSJBEJOkRC3nNVKEAk6BAMOP6XzIEEEXRksBNHKu2STCtJ1yWZckmmXQYSaWKpNNG4S188Scr1ko1m3ck76Hjn9RJQcRNPMu367QoOb5naUJKxJuXEcYSmmjBNNWGOS9XSG0v5NRZpBhLeciKWQnXwigFBBv8OQv7fRKUn5GInle3A9KzX7f66Qaq6A6+kgojUAZepapeIXA0sU9U+f9tDwDn4VV0ichoQVNUVw7zfNhEJAo14DfYHUdXbgdsBFi5cWLCqsWNhDEox1ISDnDS54ZD1rpv1xTz4Bc1B6xL+F2rKVe/L1XVJpZWYvz5zT7HMv+GhickrETkiBERwnAOJKVPNlE+SCDoOVaH8k0S+ggGHYACqOfyFSdpVkmn/55P2lqPJNNFEmoGkV/0aT7kHPof/HBAvxuBgqSe/L7LMfU2CAW+sycSG8hhrUk4iwQCRukN/d66rJNIuibR3sZBIufQnUkSTLgPxFL3x9ODfWub3FXQcQoEDv7NyrxotdlJ5AThBRGbjfeFfAXw4ewcRaQM6VNUFbsTrCQawBbhaRG7G+/kuBr6bdeiVwN1D3m8J8HHgWeAvgD+NRXvKsTYGZaw4juAgHM0tLzKJydUhCSmzfvCL1392XZIpL4kk0i5pV4+oJFEOAo4QcAIj3jPEdf3STtorHSbSLvHkgeQTTaYHv8iyE7PDgc+eST6q0BNLAsqcMh5rUs4cR6ga4Xem6v1dJtN6UAm1P54imkzTHU0OVo1mvtgCIoN/r8UuneaiqEnFb9e4DngEr0vxHaq6WkS+DixX1SXAecDNIqJ41V/X+offD5wPvIL383tYVX+XdfrLgQuHvOVPgJ+LyHq87slXFOeTHeCqsq//2ByDUgkyickMz3GEiBNgpA5YquolHT/hJl2XeNJLONGkSzSeoi+eBoT25mqmNVfOWJNKIyJeKSeIN7x8GJnSaTLlJaBoIsWAXzrtT3hjgg53gRAKFL9dR0rQMaqsLFy4UJcvX35Ex760tZM9PXHmTWk4ZsegGJPhumoXTRUgUy0a90s68WTaSzoJr20nUyWaSLucNLmB6S01R/Q+IrJCVRcOXV8ODfUVqy4cZEp79bgYg2KMJZTKMFq1aKZdJ5l2i1LitKRyFI6fVF/qEIwxJi+jtesc9fmLclZjjDHjkiUVY4wxBWNJxRhjTMFYUjHGGFMwllSMMcYUjCUVY4wxBWNJxRhjTMFYUjHGGFMw436aFhHZC2zOYdc2xvi+LDkox5igPOOymHJXjnFZTLkZy5hmquqEoSvHfVLJlYgsH26em1Iqx5igPOOymHJXjnFZTLkph5is+ssYY0zBWFIxxhhTMJZUcnd7qQMYRjnGBOUZl8WUu3KMy2LKTcljsjYVY4wxBWMlFWOMMQVjScUYY0zBWFIZhYjcISJ7ROTVUseSISLTRWSpiKwRkdUicn0ZxFQlIs+LyCo/pq+VOqYMEQmIyEoR+d9Sx5IhIptE5BUReUlEjux+1gUmIk0icr+IvCYia0XknBLHc6L/88k8ekTkM6WMKUNE/t7/O39VRO4WkZLf/lVErvfjWV3Kn5O1qYxCRN4J9AF3qerJpY4HQESmAFNU9UURqQdWABer6poSxiRArar2iUgIeBq4XlWXlSqmDBG5AVgINKjqB0sdD3hJBVioqmUzeE5E7gSeUtUfi0gYqFHVrlLHBd6FAbAdOEtVcxmsXMxYpuH9fc9X1aiI3Ac8qKo/K2FMJwP3AGcCCeBh4G9Udf1Yx2IllVGo6pNAR6njyKaqO1X1RX+5F1gLTCtxTKqqff7LkP8o+RWLiLQDHwB+XOpYypmINALvBH4CoKqJckkovncDb5Y6oWQJAtUiEgRqgB0ljmce8JyqDqhqCngCuLQUgVhSqXAiMgs4HXiutJEMVjO9BOwBHlXVkscEfBf4AuCWOpAhFPiDiKwQkWtKHQwwG9gL/NSvKvyxiNSWOqgsVwB3lzoIAFXdDtwKbAF2At2q+ofSRsWrwDtEpFVEaoALgemlCMSSSgUTkTrg18BnVLWn1PGoalpVFwDtwJl+kbxkROSDwB5VXVHKOA7j7ap6BvB+4Fq/mrWUgsAZwL+r6ulAP/DF0obk8aviPgT8qtSxAIhIM3ARXiKeCtSKyEdLGZOqrgX+GfgDXtXXS0C6FLFYUqlQfrvFr4FfquoDpY4nm19tshR4X4lDORf4kN9+cQ9wvoj8orQhefyrXVR1D/A/eHXhpbQN2JZVurwfL8mUg/cDL6rq7lIH4vszYKOq7lXVJPAAsKjEMaGqP1HVt6rqO4FO4PVSxGFJpQL5jeI/Adaq6m2ljgdARCaISJO/XA1cALxWyphU9UZVbVfVWXjVJ39S1ZJeUQKISK3fwQK/iuk9eNUXJaOqu4CtInKiv+rdQMk6fgxxJWVS9eXbApwtIjX+/+K78do1S0pEJvrPM/DaU/67FHEES/GmlURE7gbOA9pEZBvwFVX9SWmj4lzgY8ArfhsGwJdU9cESxjQFuNPvpeMA96lq2XThLTOTgP/xvo8IAv+tqg+XNiQAPg380q9u2gB8ssTxZJLuBcCnSh1Lhqo+JyL3Ay8CKWAlZTA9CvBrEWkFksC1pepoYV2KjTHGFIxVfxljjCkYSyrGGGMKxpKKMcaYgrGkYowxpmAsqRhjjCkYSyrGGGMKxpKKMVlEpG/0vcqfiDzoT2XfJCJ/m7V+qj/GwpiisHEqxmQRkT5VrRuD9wn6s8kW+31mAf9bLrdtMMc+K6kYMwoROU5EHvZnFH5KRE4SkUYR2Swijr9PrYhsFZHQcPv7+/xMRP5DRJ4DbjnMe31VRH4uIs+KyBsicrW/XkTkX/ybML0iIn/lr58iIk/6N7F6VUTe4a/fJCJtwLeB4/zt/yIis8S/4Zx4N1b7qX++lSLyLn/9J0TkAf8zvCEiw8ZqzHBsmhZjRnc73g2P3hCRs4B/U9Xz/SlyFuNNnvlB4BFVTYrIIfsD5/vnagcWqepIM8ieCpwN1AIrReT3wDnAAuA0oA14QUSeBD7sv+83/Slyaoac64vAyf7s0ZmSS8a1eLfCOcVPfH8Qkbn+tgV4t1SIA+tE5AequjX3H5kZryypGDMC//YCi4Bf+XN1AUT853uBv8JLKlcA/zbK/gC/GiWhAPxWVaNAVESW4s1g/Hbgbv/Y3SLyBPA24AXgDn/W6t+o6kuHPeuh3g78AEBVXxORzUAmqTymqt3+z2ANMBOwpGJGZUnFmJE5QFfmSn+IJcC3RKQFeCvwJ7zSxeH2B+8+JaMZ2tB52IZPVX3SvxfLB4CfichtqnpXDu8xmnjWchr7rjA5sjYVY0bg3/xso4j8JQy2bZzmb+vDKyl8D68xPD3S/nm4yG/vaMWbIfsF4Cngr8S7u+YEvFv/Pi8iM4HdqvpfeLdMHnoPlF6g/jDv8xTwET/OucAMYF2esRpzEEsqxhysRkS2ZT1uwPvivUpEVgGr8e76l3Ev8FH/OWOk/XPxMl6V2jLgn1R1B96NvF4GVuGViL7g3wPlPGCViKzEq4r7XvaJVHU/8IzfiP8vQ97n3wBHRF7x4/+EqsYx5ihYl2JjyoiIfBXoU9VbSx2LMUfCSirGGGMKxhrfjCkBEfkkcP2Q1c+o6rWliMeYQrHqL2OMMQVj1V/GGGMKxpKKMcaYgrGkYowxpmAsqRhjjCmY/x/hXF9sQt4nJgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bpxZQwscpE5z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "d0512f51-b029-417c-d488-f4a5ec97199b"
      },
      "source": [
        "sns.distplot(data.Gas_generator_rate_of_revolutions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7693bc8dd8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_y97UWeGob3W"
      },
      "source": [
        "Feature Selection\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHjQGhvIokHF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "2418d670-6da4-4031-ca25-2efa2740df90"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Lever_position</th>\n",
              "      <th>Ship_speed</th>\n",
              "      <th>Gas_turbine_shaft_torque</th>\n",
              "      <th>Gas_turbine_rate_of_revolutions</th>\n",
              "      <th>Gas_generator_rate_of_revolutions</th>\n",
              "      <th>Starboard_propeller_torque</th>\n",
              "      <th>Port_propeller_torque</th>\n",
              "      <th>HP_turbine_exit_temperature</th>\n",
              "      <th>GT_compressor_inlet_air_temperature</th>\n",
              "      <th>GT_compressor_outlet_air_temperature</th>\n",
              "      <th>HP_turbine_exit_pressure</th>\n",
              "      <th>GT_compressor_inlet_air_pressure</th>\n",
              "      <th>GT_compressor_outlet_air_pressure</th>\n",
              "      <th>Gas_turbine_exhaust_gas_pressure</th>\n",
              "      <th>Turbine_injection_control</th>\n",
              "      <th>Fuel_flow</th>\n",
              "      <th>GT_compressor_decay_state_coefficient</th>\n",
              "      <th>GT_turbine_decay_state_coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>1.000000e+04</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.150075</td>\n",
              "      <td>14.951400</td>\n",
              "      <td>27100.647714</td>\n",
              "      <td>2131.151788</td>\n",
              "      <td>8194.182366</td>\n",
              "      <td>225.957785</td>\n",
              "      <td>225.957785</td>\n",
              "      <td>734.476178</td>\n",
              "      <td>288.0</td>\n",
              "      <td>645.748980</td>\n",
              "      <td>2.345732</td>\n",
              "      <td>9.980000e-01</td>\n",
              "      <td>12.262597</td>\n",
              "      <td>1.029402</td>\n",
              "      <td>33.480715</td>\n",
              "      <td>0.659100</td>\n",
              "      <td>0.975021</td>\n",
              "      <td>0.987455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.624015</td>\n",
              "      <td>7.740467</td>\n",
              "      <td>22062.780307</td>\n",
              "      <td>772.211732</td>\n",
              "      <td>1090.569425</td>\n",
              "      <td>199.737886</td>\n",
              "      <td>199.737886</td>\n",
              "      <td>173.009157</td>\n",
              "      <td>0.0</td>\n",
              "      <td>72.483036</td>\n",
              "      <td>1.081038</td>\n",
              "      <td>2.236101e-13</td>\n",
              "      <td>5.320817</td>\n",
              "      <td>0.010353</td>\n",
              "      <td>25.721725</td>\n",
              "      <td>0.504791</td>\n",
              "      <td>0.014734</td>\n",
              "      <td>0.007510</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.138000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>253.547000</td>\n",
              "      <td>1307.675000</td>\n",
              "      <td>6589.002000</td>\n",
              "      <td>5.304000</td>\n",
              "      <td>5.304000</td>\n",
              "      <td>444.731000</td>\n",
              "      <td>288.0</td>\n",
              "      <td>540.999000</td>\n",
              "      <td>1.093000</td>\n",
              "      <td>9.980000e-01</td>\n",
              "      <td>5.828000</td>\n",
              "      <td>1.019000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.068000</td>\n",
              "      <td>0.950000</td>\n",
              "      <td>0.975000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.144000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>8375.791500</td>\n",
              "      <td>1386.758000</td>\n",
              "      <td>7056.520500</td>\n",
              "      <td>60.317000</td>\n",
              "      <td>60.317000</td>\n",
              "      <td>589.841000</td>\n",
              "      <td>288.0</td>\n",
              "      <td>577.980750</td>\n",
              "      <td>1.389000</td>\n",
              "      <td>9.980000e-01</td>\n",
              "      <td>7.445000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>13.534000</td>\n",
              "      <td>0.246000</td>\n",
              "      <td>0.962000</td>\n",
              "      <td>0.981000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.140000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>21630.133500</td>\n",
              "      <td>1924.325000</td>\n",
              "      <td>8480.527000</td>\n",
              "      <td>175.264000</td>\n",
              "      <td>175.264000</td>\n",
              "      <td>705.351000</td>\n",
              "      <td>288.0</td>\n",
              "      <td>636.800000</td>\n",
              "      <td>2.082000</td>\n",
              "      <td>9.980000e-01</td>\n",
              "      <td>11.086000</td>\n",
              "      <td>1.026000</td>\n",
              "      <td>25.249500</td>\n",
              "      <td>0.496000</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.987000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.148000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>39000.710250</td>\n",
              "      <td>2678.078000</td>\n",
              "      <td>9132.132500</td>\n",
              "      <td>332.360000</td>\n",
              "      <td>332.360000</td>\n",
              "      <td>833.154750</td>\n",
              "      <td>288.0</td>\n",
              "      <td>693.656750</td>\n",
              "      <td>2.981000</td>\n",
              "      <td>9.980000e-01</td>\n",
              "      <td>15.649000</td>\n",
              "      <td>1.036000</td>\n",
              "      <td>44.500000</td>\n",
              "      <td>0.881000</td>\n",
              "      <td>0.988000</td>\n",
              "      <td>0.994000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.300000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>72783.338000</td>\n",
              "      <td>3560.741000</td>\n",
              "      <td>9797.103000</td>\n",
              "      <td>645.249000</td>\n",
              "      <td>645.249000</td>\n",
              "      <td>1115.797000</td>\n",
              "      <td>288.0</td>\n",
              "      <td>789.094000</td>\n",
              "      <td>4.560000</td>\n",
              "      <td>9.980000e-01</td>\n",
              "      <td>23.140000</td>\n",
              "      <td>1.052000</td>\n",
              "      <td>92.556000</td>\n",
              "      <td>1.832000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Lever_position  ...  GT_turbine_decay_state_coefficient\n",
              "count    10000.000000  ...                        10000.000000\n",
              "mean         5.150075  ...                            0.987455\n",
              "std          2.624015  ...                            0.007510\n",
              "min          1.138000  ...                            0.975000\n",
              "25%          3.144000  ...                            0.981000\n",
              "50%          5.140000  ...                            0.987000\n",
              "75%          7.148000  ...                            0.994000\n",
              "max          9.300000  ...                            1.000000\n",
              "\n",
              "[8 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IxYlCPaxqdlD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "outputId": "f095990e-3a4f-4ca7-bbce-30f73f60eaca"
      },
      "source": [
        "#Calculate pairwise correlation of column, default = 'pearson' standard correlation coefficient\n",
        "np.round(data.corr(method = 'pearson'),4) #correlation matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Lever_position</th>\n",
              "      <th>Ship_speed</th>\n",
              "      <th>Gas_turbine_shaft_torque</th>\n",
              "      <th>Gas_turbine_rate_of_revolutions</th>\n",
              "      <th>Gas_generator_rate_of_revolutions</th>\n",
              "      <th>Starboard_propeller_torque</th>\n",
              "      <th>Port_propeller_torque</th>\n",
              "      <th>HP_turbine_exit_temperature</th>\n",
              "      <th>GT_compressor_inlet_air_temperature</th>\n",
              "      <th>GT_compressor_outlet_air_temperature</th>\n",
              "      <th>HP_turbine_exit_pressure</th>\n",
              "      <th>GT_compressor_inlet_air_pressure</th>\n",
              "      <th>GT_compressor_outlet_air_pressure</th>\n",
              "      <th>Gas_turbine_exhaust_gas_pressure</th>\n",
              "      <th>Turbine_injection_control</th>\n",
              "      <th>Fuel_flow</th>\n",
              "      <th>GT_compressor_decay_state_coefficient</th>\n",
              "      <th>GT_turbine_decay_state_coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Lever_position</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9999</td>\n",
              "      <td>0.9609</td>\n",
              "      <td>0.9617</td>\n",
              "      <td>0.9861</td>\n",
              "      <td>0.9592</td>\n",
              "      <td>0.9592</td>\n",
              "      <td>0.9607</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9826</td>\n",
              "      <td>0.9629</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.9689</td>\n",
              "      <td>0.9532</td>\n",
              "      <td>0.9126</td>\n",
              "      <td>0.9310</td>\n",
              "      <td>-0.0021</td>\n",
              "      <td>-0.0010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ship_speed</th>\n",
              "      <td>0.9999</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9581</td>\n",
              "      <td>0.9601</td>\n",
              "      <td>0.9867</td>\n",
              "      <td>0.9564</td>\n",
              "      <td>0.9564</td>\n",
              "      <td>0.9583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9810</td>\n",
              "      <td>0.9604</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9668</td>\n",
              "      <td>0.9506</td>\n",
              "      <td>0.9090</td>\n",
              "      <td>0.9274</td>\n",
              "      <td>-0.0020</td>\n",
              "      <td>-0.0010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gas_turbine_shaft_torque</th>\n",
              "      <td>0.9609</td>\n",
              "      <td>0.9581</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9897</td>\n",
              "      <td>0.9332</td>\n",
              "      <td>0.9992</td>\n",
              "      <td>0.9992</td>\n",
              "      <td>0.9911</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9903</td>\n",
              "      <td>0.9989</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.9976</td>\n",
              "      <td>0.9960</td>\n",
              "      <td>0.9774</td>\n",
              "      <td>0.9950</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>-0.0005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gas_turbine_rate_of_revolutions</th>\n",
              "      <td>0.9617</td>\n",
              "      <td>0.9601</td>\n",
              "      <td>0.9897</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9429</td>\n",
              "      <td>0.9886</td>\n",
              "      <td>0.9886</td>\n",
              "      <td>0.9794</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9892</td>\n",
              "      <td>0.9951</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9960</td>\n",
              "      <td>0.9940</td>\n",
              "      <td>0.9619</td>\n",
              "      <td>0.9801</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>-0.0007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gas_generator_rate_of_revolutions</th>\n",
              "      <td>0.9861</td>\n",
              "      <td>0.9867</td>\n",
              "      <td>0.9332</td>\n",
              "      <td>0.9429</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9299</td>\n",
              "      <td>0.9299</td>\n",
              "      <td>0.9395</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9667</td>\n",
              "      <td>0.9376</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9460</td>\n",
              "      <td>0.9244</td>\n",
              "      <td>0.8786</td>\n",
              "      <td>0.8969</td>\n",
              "      <td>-0.0209</td>\n",
              "      <td>0.0087</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Starboard_propeller_torque</th>\n",
              "      <td>0.9592</td>\n",
              "      <td>0.9564</td>\n",
              "      <td>0.9992</td>\n",
              "      <td>0.9886</td>\n",
              "      <td>0.9299</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9859</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9875</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.9962</td>\n",
              "      <td>0.9962</td>\n",
              "      <td>0.9770</td>\n",
              "      <td>0.9943</td>\n",
              "      <td>-0.0016</td>\n",
              "      <td>-0.0007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Port_propeller_torque</th>\n",
              "      <td>0.9592</td>\n",
              "      <td>0.9564</td>\n",
              "      <td>0.9992</td>\n",
              "      <td>0.9886</td>\n",
              "      <td>0.9299</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9859</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9875</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.9962</td>\n",
              "      <td>0.9962</td>\n",
              "      <td>0.9770</td>\n",
              "      <td>0.9943</td>\n",
              "      <td>-0.0016</td>\n",
              "      <td>-0.0007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HP_turbine_exit_temperature</th>\n",
              "      <td>0.9607</td>\n",
              "      <td>0.9583</td>\n",
              "      <td>0.9911</td>\n",
              "      <td>0.9794</td>\n",
              "      <td>0.9395</td>\n",
              "      <td>0.9859</td>\n",
              "      <td>0.9859</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9922</td>\n",
              "      <td>0.9893</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.9904</td>\n",
              "      <td>0.9800</td>\n",
              "      <td>0.9693</td>\n",
              "      <td>0.9863</td>\n",
              "      <td>-0.0417</td>\n",
              "      <td>-0.0399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GT_compressor_inlet_air_temperature</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GT_compressor_outlet_air_temperature</th>\n",
              "      <td>0.9826</td>\n",
              "      <td>0.9810</td>\n",
              "      <td>0.9903</td>\n",
              "      <td>0.9892</td>\n",
              "      <td>0.9667</td>\n",
              "      <td>0.9875</td>\n",
              "      <td>0.9875</td>\n",
              "      <td>0.9922</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9917</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9944</td>\n",
              "      <td>0.9835</td>\n",
              "      <td>0.9582</td>\n",
              "      <td>0.9764</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>-0.0181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HP_turbine_exit_pressure</th>\n",
              "      <td>0.9629</td>\n",
              "      <td>0.9604</td>\n",
              "      <td>0.9989</td>\n",
              "      <td>0.9951</td>\n",
              "      <td>0.9376</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>0.9980</td>\n",
              "      <td>0.9893</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9917</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9994</td>\n",
              "      <td>0.9979</td>\n",
              "      <td>0.9753</td>\n",
              "      <td>0.9927</td>\n",
              "      <td>0.0063</td>\n",
              "      <td>-0.0035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GT_compressor_inlet_air_pressure</th>\n",
              "      <td>-0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>-0.0000</td>\n",
              "      <td>-0.0000</td>\n",
              "      <td>-0.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.0000</td>\n",
              "      <td>-0.0000</td>\n",
              "      <td>-0.0000</td>\n",
              "      <td>-0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GT_compressor_outlet_air_pressure</th>\n",
              "      <td>0.9689</td>\n",
              "      <td>0.9668</td>\n",
              "      <td>0.9976</td>\n",
              "      <td>0.9960</td>\n",
              "      <td>0.9460</td>\n",
              "      <td>0.9962</td>\n",
              "      <td>0.9962</td>\n",
              "      <td>0.9904</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9944</td>\n",
              "      <td>0.9994</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9963</td>\n",
              "      <td>0.9717</td>\n",
              "      <td>0.9892</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>-0.0192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gas_turbine_exhaust_gas_pressure</th>\n",
              "      <td>0.9532</td>\n",
              "      <td>0.9506</td>\n",
              "      <td>0.9960</td>\n",
              "      <td>0.9940</td>\n",
              "      <td>0.9244</td>\n",
              "      <td>0.9962</td>\n",
              "      <td>0.9962</td>\n",
              "      <td>0.9800</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9835</td>\n",
              "      <td>0.9979</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.9963</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9738</td>\n",
              "      <td>0.9909</td>\n",
              "      <td>0.0334</td>\n",
              "      <td>0.0113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Turbine_injection_control</th>\n",
              "      <td>0.9126</td>\n",
              "      <td>0.9090</td>\n",
              "      <td>0.9774</td>\n",
              "      <td>0.9619</td>\n",
              "      <td>0.8786</td>\n",
              "      <td>0.9770</td>\n",
              "      <td>0.9770</td>\n",
              "      <td>0.9693</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9582</td>\n",
              "      <td>0.9753</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.9717</td>\n",
              "      <td>0.9738</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.9853</td>\n",
              "      <td>-0.0341</td>\n",
              "      <td>-0.0192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fuel_flow</th>\n",
              "      <td>0.9310</td>\n",
              "      <td>0.9274</td>\n",
              "      <td>0.9950</td>\n",
              "      <td>0.9801</td>\n",
              "      <td>0.8969</td>\n",
              "      <td>0.9943</td>\n",
              "      <td>0.9943</td>\n",
              "      <td>0.9863</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.9764</td>\n",
              "      <td>0.9927</td>\n",
              "      <td>-0.0</td>\n",
              "      <td>0.9892</td>\n",
              "      <td>0.9909</td>\n",
              "      <td>0.9853</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>-0.0158</td>\n",
              "      <td>-0.0182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GT_compressor_decay_state_coefficient</th>\n",
              "      <td>-0.0021</td>\n",
              "      <td>-0.0020</td>\n",
              "      <td>0.0007</td>\n",
              "      <td>0.0002</td>\n",
              "      <td>-0.0209</td>\n",
              "      <td>-0.0016</td>\n",
              "      <td>-0.0016</td>\n",
              "      <td>-0.0417</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>0.0063</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>0.0334</td>\n",
              "      <td>-0.0341</td>\n",
              "      <td>-0.0158</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>-0.0010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GT_turbine_decay_state_coefficient</th>\n",
              "      <td>-0.0010</td>\n",
              "      <td>-0.0010</td>\n",
              "      <td>-0.0005</td>\n",
              "      <td>-0.0007</td>\n",
              "      <td>0.0087</td>\n",
              "      <td>-0.0007</td>\n",
              "      <td>-0.0007</td>\n",
              "      <td>-0.0399</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.0181</td>\n",
              "      <td>-0.0035</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.0192</td>\n",
              "      <td>0.0113</td>\n",
              "      <td>-0.0192</td>\n",
              "      <td>-0.0182</td>\n",
              "      <td>-0.0010</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                       Lever_position  ...  GT_turbine_decay_state_coefficient\n",
              "Lever_position                                 1.0000  ...                             -0.0010\n",
              "Ship_speed                                     0.9999  ...                             -0.0010\n",
              "Gas_turbine_shaft_torque                       0.9609  ...                             -0.0005\n",
              "Gas_turbine_rate_of_revolutions                0.9617  ...                             -0.0007\n",
              "Gas_generator_rate_of_revolutions              0.9861  ...                              0.0087\n",
              "Starboard_propeller_torque                     0.9592  ...                             -0.0007\n",
              "Port_propeller_torque                          0.9592  ...                             -0.0007\n",
              "HP_turbine_exit_temperature                    0.9607  ...                             -0.0399\n",
              "GT_compressor_inlet_air_temperature               NaN  ...                                 NaN\n",
              "GT_compressor_outlet_air_temperature           0.9826  ...                             -0.0181\n",
              "HP_turbine_exit_pressure                       0.9629  ...                             -0.0035\n",
              "GT_compressor_inlet_air_pressure              -0.0000  ...                              0.0000\n",
              "GT_compressor_outlet_air_pressure              0.9689  ...                             -0.0192\n",
              "Gas_turbine_exhaust_gas_pressure               0.9532  ...                              0.0113\n",
              "Turbine_injection_control                      0.9126  ...                             -0.0192\n",
              "Fuel_flow                                      0.9310  ...                             -0.0182\n",
              "GT_compressor_decay_state_coefficient         -0.0021  ...                             -0.0010\n",
              "GT_turbine_decay_state_coefficient            -0.0010  ...                              1.0000\n",
              "\n",
              "[18 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZrPpf4iiot7Y"
      },
      "source": [
        "#In above correlation matrix, we see GT_compressor_inlet_air_temperature, GT_compressor_inlet_air_pressure = 0\n",
        "data = data.drop('GT_compressor_inlet_air_temperature', axis=1)\n",
        "data = data.drop('GT_compressor_inlet_air_pressure',axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bFrNGE1bE8k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "4193bf86-fb5f-481e-d567-c08595409cd0"
      },
      "source": [
        "#Split data into X(features),Y(results)\n",
        "X = data[['Lever_position', 'Ship_speed', 'Gas_turbine_shaft_torque',\n",
        "          'Gas_turbine_rate_of_revolutions', 'Gas_generator_rate_of_revolutions',\n",
        "          'Starboard_propeller_torque', 'Port_propeller_torque',\n",
        "          'HP_turbine_exit_temperature', 'GT_compressor_outlet_air_temperature', \n",
        "          'HP_turbine_exit_pressure', 'GT_compressor_outlet_air_pressure',\n",
        "          'Gas_turbine_exhaust_gas_pressure', 'Turbine_injection_control',\n",
        "          'Fuel_flow', ]]\n",
        "Y1=data['GT_compressor_decay_state_coefficient']\n",
        "Y2=data['GT_turbine_decay_state_coefficient']\n",
        "Y = pd.DataFrame([Y1,Y2]).transpose()\n",
        "Y"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GT_compressor_decay_state_coefficient</th>\n",
              "      <th>GT_turbine_decay_state_coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.973</td>\n",
              "      <td>0.978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.995</td>\n",
              "      <td>0.975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.980</td>\n",
              "      <td>0.978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.984</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.998</td>\n",
              "      <td>0.980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>0.999</td>\n",
              "      <td>0.988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>0.971</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>0.953</td>\n",
              "      <td>0.996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>0.982</td>\n",
              "      <td>0.975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>0.981</td>\n",
              "      <td>0.977</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      GT_compressor_decay_state_coefficient  GT_turbine_decay_state_coefficient\n",
              "0                                     0.973                               0.978\n",
              "1                                     0.995                               0.975\n",
              "2                                     0.980                               0.978\n",
              "3                                     0.984                               1.000\n",
              "4                                     0.998                               0.980\n",
              "...                                     ...                                 ...\n",
              "9995                                  0.999                               0.988\n",
              "9996                                  0.971                               1.000\n",
              "9997                                  0.953                               0.996\n",
              "9998                                  0.982                               0.975\n",
              "9999                                  0.981                               0.977\n",
              "\n",
              "[10000 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_RbxELibgOCl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "outputId": "f2faca5a-1fde-49fb-cfcf-73b794f746fc"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "var = ['Lever_position', 'Ship_speed', 'Gas_turbine_shaft_torque',\n",
        "       'Gas_turbine_rate_of_revolutions', 'Gas_generator_rate_of_revolutions',\n",
        "       'Starboard_propeller_torque', 'Port_propeller_torque',\n",
        "       'HP_turbine_exit_temperature', 'GT_compressor_outlet_air_temperature',\n",
        "       'HP_turbine_exit_pressure', 'GT_compressor_outlet_air_pressure',\n",
        "       'Gas_turbine_exhaust_gas_pressure', 'Turbine_injection_control',\n",
        "       'Fuel_flow', 'GT_compressor_decay_state_coefficient',\n",
        "       'GT_turbine_decay_state_coefficient']\n",
        "data1 = data\n",
        "data1[var] = scaler.fit_transform(data1[var])\n",
        "data1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Lever_position</th>\n",
              "      <th>Ship_speed</th>\n",
              "      <th>Gas_turbine_shaft_torque</th>\n",
              "      <th>Gas_turbine_rate_of_revolutions</th>\n",
              "      <th>Gas_generator_rate_of_revolutions</th>\n",
              "      <th>Starboard_propeller_torque</th>\n",
              "      <th>Port_propeller_torque</th>\n",
              "      <th>HP_turbine_exit_temperature</th>\n",
              "      <th>GT_compressor_outlet_air_temperature</th>\n",
              "      <th>HP_turbine_exit_pressure</th>\n",
              "      <th>GT_compressor_outlet_air_pressure</th>\n",
              "      <th>Gas_turbine_exhaust_gas_pressure</th>\n",
              "      <th>Turbine_injection_control</th>\n",
              "      <th>Fuel_flow</th>\n",
              "      <th>GT_compressor_decay_state_coefficient</th>\n",
              "      <th>GT_turbine_decay_state_coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.581596</td>\n",
              "      <td>1.556651</td>\n",
              "      <td>2.069723</td>\n",
              "      <td>1.850934</td>\n",
              "      <td>1.430177</td>\n",
              "      <td>2.097094</td>\n",
              "      <td>2.097094</td>\n",
              "      <td>2.035294</td>\n",
              "      <td>1.856458</td>\n",
              "      <td>2.014154</td>\n",
              "      <td>1.995358</td>\n",
              "      <td>1.989602</td>\n",
              "      <td>2.214359</td>\n",
              "      <td>2.240446</td>\n",
              "      <td>-0.137159</td>\n",
              "      <td>-1.259128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.390614</td>\n",
              "      <td>0.393872</td>\n",
              "      <td>0.120567</td>\n",
              "      <td>0.227505</td>\n",
              "      <td>0.537205</td>\n",
              "      <td>0.100403</td>\n",
              "      <td>0.100403</td>\n",
              "      <td>0.245345</td>\n",
              "      <td>0.272657</td>\n",
              "      <td>0.159362</td>\n",
              "      <td>0.220918</td>\n",
              "      <td>0.057762</td>\n",
              "      <td>0.043362</td>\n",
              "      <td>0.051310</td>\n",
              "      <td>1.356065</td>\n",
              "      <td>-1.658640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.764544</td>\n",
              "      <td>-0.768907</td>\n",
              "      <td>-0.848751</td>\n",
              "      <td>-0.964049</td>\n",
              "      <td>-1.047727</td>\n",
              "      <td>-0.829327</td>\n",
              "      <td>-0.829327</td>\n",
              "      <td>-0.836484</td>\n",
              "      <td>-0.899041</td>\n",
              "      <td>-0.882282</td>\n",
              "      <td>-0.882727</td>\n",
              "      <td>-0.908158</td>\n",
              "      <td>-0.816616</td>\n",
              "      <td>-0.816420</td>\n",
              "      <td>0.337958</td>\n",
              "      <td>-1.259128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.764544</td>\n",
              "      <td>-0.768907</td>\n",
              "      <td>-0.848669</td>\n",
              "      <td>-0.964037</td>\n",
              "      <td>-1.004767</td>\n",
              "      <td>-0.829222</td>\n",
              "      <td>-0.829222</td>\n",
              "      <td>-0.946964</td>\n",
              "      <td>-0.954533</td>\n",
              "      <td>-0.884132</td>\n",
              "      <td>-0.912236</td>\n",
              "      <td>-0.811566</td>\n",
              "      <td>-0.847681</td>\n",
              "      <td>-0.848118</td>\n",
              "      <td>0.609453</td>\n",
              "      <td>1.670625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.390614</td>\n",
              "      <td>0.393872</td>\n",
              "      <td>0.120591</td>\n",
              "      <td>0.227505</td>\n",
              "      <td>0.539050</td>\n",
              "      <td>0.100453</td>\n",
              "      <td>0.100453</td>\n",
              "      <td>0.204501</td>\n",
              "      <td>0.247422</td>\n",
              "      <td>0.159362</td>\n",
              "      <td>0.208889</td>\n",
              "      <td>0.154354</td>\n",
              "      <td>0.026177</td>\n",
              "      <td>0.033480</td>\n",
              "      <td>1.559687</td>\n",
              "      <td>-0.992787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>-1.167001</td>\n",
              "      <td>-1.156500</td>\n",
              "      <td>-0.962840</td>\n",
              "      <td>-1.012893</td>\n",
              "      <td>-1.336900</td>\n",
              "      <td>-1.015315</td>\n",
              "      <td>-1.015315</td>\n",
              "      <td>-0.887045</td>\n",
              "      <td>-1.115172</td>\n",
              "      <td>-0.972940</td>\n",
              "      <td>-0.990799</td>\n",
              "      <td>-1.004750</td>\n",
              "      <td>-0.462578</td>\n",
              "      <td>-0.820382</td>\n",
              "      <td>1.627561</td>\n",
              "      <td>0.072578</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>-0.003840</td>\n",
              "      <td>0.006279</td>\n",
              "      <td>-0.247801</td>\n",
              "      <td>-0.267819</td>\n",
              "      <td>0.277828</td>\n",
              "      <td>-0.253694</td>\n",
              "      <td>-0.253694</td>\n",
              "      <td>-0.221064</td>\n",
              "      <td>-0.135969</td>\n",
              "      <td>-0.247675</td>\n",
              "      <td>-0.247267</td>\n",
              "      <td>-0.328606</td>\n",
              "      <td>-0.349905</td>\n",
              "      <td>-0.344914</td>\n",
              "      <td>-0.272907</td>\n",
              "      <td>1.670625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>1.164657</td>\n",
              "      <td>1.169058</td>\n",
              "      <td>1.083062</td>\n",
              "      <td>1.238587</td>\n",
              "      <td>1.036458</td>\n",
              "      <td>1.061911</td>\n",
              "      <td>1.061911</td>\n",
              "      <td>1.121701</td>\n",
              "      <td>1.265104</td>\n",
              "      <td>1.123299</td>\n",
              "      <td>1.115938</td>\n",
              "      <td>1.120274</td>\n",
              "      <td>1.042956</td>\n",
              "      <td>1.059700</td>\n",
              "      <td>-1.494637</td>\n",
              "      <td>1.137943</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>0.390614</td>\n",
              "      <td>0.393872</td>\n",
              "      <td>0.120591</td>\n",
              "      <td>0.227537</td>\n",
              "      <td>0.549392</td>\n",
              "      <td>0.100212</td>\n",
              "      <td>0.100212</td>\n",
              "      <td>0.283316</td>\n",
              "      <td>0.314697</td>\n",
              "      <td>0.154737</td>\n",
              "      <td>0.216031</td>\n",
              "      <td>0.057762</td>\n",
              "      <td>0.055920</td>\n",
              "      <td>0.063197</td>\n",
              "      <td>0.473705</td>\n",
              "      <td>-1.658640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>1.164657</td>\n",
              "      <td>1.169058</td>\n",
              "      <td>1.082867</td>\n",
              "      <td>1.238294</td>\n",
              "      <td>1.014763</td>\n",
              "      <td>1.061811</td>\n",
              "      <td>1.061811</td>\n",
              "      <td>1.126123</td>\n",
              "      <td>1.184626</td>\n",
              "      <td>1.157527</td>\n",
              "      <td>1.208785</td>\n",
              "      <td>1.120274</td>\n",
              "      <td>1.071260</td>\n",
              "      <td>1.087435</td>\n",
              "      <td>0.405831</td>\n",
              "      <td>-1.392298</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Lever_position  ...  GT_turbine_decay_state_coefficient\n",
              "0           1.581596  ...                           -1.259128\n",
              "1           0.390614  ...                           -1.658640\n",
              "2          -0.764544  ...                           -1.259128\n",
              "3          -0.764544  ...                            1.670625\n",
              "4           0.390614  ...                           -0.992787\n",
              "...              ...  ...                                 ...\n",
              "9995       -1.167001  ...                            0.072578\n",
              "9996       -0.003840  ...                            1.670625\n",
              "9997        1.164657  ...                            1.137943\n",
              "9998        0.390614  ...                           -1.658640\n",
              "9999        1.164657  ...                           -1.392298\n",
              "\n",
              "[10000 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "shKsjQxcqLz7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "0a2eb1d7-edf0-4ac1-e9d3-451996a7e78b"
      },
      "source": [
        "corr_mat= np.round(data.corr(method = 'pearson'),4)\n",
        "plt.figure(figsize = (18,9))\n",
        "sns.heatmap(corr_mat, annot = True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "b6CW17zuIApc",
        "outputId": "bf80f331-8e6b-499b-b373-e9fc4ac24d99"
      },
      "source": [
        "corr_mat= Y.corr()\n",
        "plt.figure(figsize = (18,9))\n",
        "sns.heatmap(corr_mat, annot = True)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eF_aKkKhi2VU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "24e69862-9473-425d-e17d-afe8ca18da8e"
      },
      "source": [
        "vif = pd.DataFrame()\n",
        "vif['Features'] = data1.columns\n",
        "vif['VIF'] = [variance_inflation_factor(data.values, i) for i in range(data.shape[1])]\n",
        "vif['VIF'] = round(vif['VIF'], 2)\n",
        "vif = vif.sort_values(by = \"VIF\", ascending = False)\n",
        "vif"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/stats/outliers_influence.py:185: RuntimeWarning: divide by zero encountered in double_scalars\n",
            "  vif = 1. / (1. - r_squared_i)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Features</th>\n",
              "      <th>VIF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Starboard_propeller_torque</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Port_propeller_torque</td>\n",
              "      <td>inf</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Lever_position</td>\n",
              "      <td>64865.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Ship_speed</td>\n",
              "      <td>49209.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gas_turbine_shaft_torque</td>\n",
              "      <td>48092.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>HP_turbine_exit_pressure</td>\n",
              "      <td>38572.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>GT_compressor_outlet_air_pressure</td>\n",
              "      <td>29269.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Fuel_flow</td>\n",
              "      <td>25139.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>HP_turbine_exit_temperature</td>\n",
              "      <td>7933.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>GT_compressor_outlet_air_temperature</td>\n",
              "      <td>3947.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Gas_turbine_rate_of_revolutions</td>\n",
              "      <td>2268.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Gas_turbine_exhaust_gas_pressure</td>\n",
              "      <td>1318.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gas_generator_rate_of_revolutions</td>\n",
              "      <td>876.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Turbine_injection_control</td>\n",
              "      <td>108.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>GT_turbine_decay_state_coefficient</td>\n",
              "      <td>17.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>GT_compressor_decay_state_coefficient</td>\n",
              "      <td>10.16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 Features       VIF\n",
              "5              Starboard_propeller_torque       inf\n",
              "6                   Port_propeller_torque       inf\n",
              "0                          Lever_position  64865.33\n",
              "1                              Ship_speed  49209.07\n",
              "2                Gas_turbine_shaft_torque  48092.07\n",
              "9                HP_turbine_exit_pressure  38572.28\n",
              "10      GT_compressor_outlet_air_pressure  29269.76\n",
              "13                              Fuel_flow  25139.45\n",
              "7             HP_turbine_exit_temperature   7933.99\n",
              "8    GT_compressor_outlet_air_temperature   3947.47\n",
              "3         Gas_turbine_rate_of_revolutions   2268.00\n",
              "11       Gas_turbine_exhaust_gas_pressure   1318.26\n",
              "4       Gas_generator_rate_of_revolutions    876.46\n",
              "12              Turbine_injection_control    108.17\n",
              "15     GT_turbine_decay_state_coefficient     17.99\n",
              "14  GT_compressor_decay_state_coefficient     10.16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHJ9r0hK7I7_"
      },
      "source": [
        "#Split X,Y into train, test (70-30)\n",
        "X_train1, X_test1, y1_train, y1_test = train_test_split(X, Y1, test_size = 0.3)\n",
        "X_train2, X_test2, y2_train, y2_test = train_test_split(X, Y2, test_size = 0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ATxXjnOo8hW-"
      },
      "source": [
        "models = []\n",
        "models.append(('Linear Regression', LinearRegression()))\n",
        "models.append(('KNN', KNeighborsRegressor()))\n",
        "models.append(('Decision', DecisionTreeRegressor()))\n",
        "models.append(('Random Forest', RandomForestRegressor()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dt-PAw1Vs_FZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fddfdb4b-4080-46d6-d2d1-789d80bc7eae"
      },
      "source": [
        "#For compressor decay \n",
        "results = []\n",
        "names = []\n",
        "scoring = []\n",
        "for name, model in models:\n",
        "    # Fit the model\n",
        "    model.fit(X_train1, y1_train)\n",
        "    predictions = model.predict(X_test1)\n",
        "    predictions_train = model.predict(X_train1)\n",
        "    # Evaluate the model\n",
        "    score = r2_score(y1_test, predictions)\n",
        "    train_score = r2_score(y1_train, predictions_train)\n",
        "    mse = mean_squared_error(predictions, y1_test)\n",
        "    results.append(mse)\n",
        "    names.append(name)\n",
        "    msg = \"%s: Train %f, Test %f, Error %f\" %(name, train_score, score, mse)\n",
        "    print(msg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear Regression: Train 0.843676, Test 0.840362, Error 0.163777\n",
            "KNN: Train 0.955039, Test 0.918073, Error 0.084051\n",
            "Decision: Train 1.000000, Test 0.984062, Error 0.016351\n",
            "Random Forest: Train 0.999092, Test 0.994253, Error 0.005896\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j-7H4yWcyt6i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "deadd499-17fa-4c50-c570-5c9f7fc3277f"
      },
      "source": [
        "#For turbine decay \n",
        "results = []\n",
        "names = []\n",
        "scoring = []\n",
        "for name, model in models:\n",
        "    # Fit the model\n",
        "    model.fit(X_train2, y2_train)\n",
        "    predictions = model.predict(X_test2)\n",
        "    predictions_train = model.predict(X_train2)\n",
        "    # Evaluate the model\n",
        "    score = r2_score(y2_test, predictions)\n",
        "    train_score = r2_score(y2_train, predictions_train)\n",
        "    results.append(mse)\n",
        "    names.append(name)\n",
        "    msg = \"%s: Train %f, Test %f, Error %f\" %(name, train_score, score, mse)\n",
        "    print(msg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Linear Regression: Train 0.911244, Test 0.910862, Error 0.005896\n",
            "KNN: Train 0.914175, Test 0.860251, Error 0.005896\n",
            "Decision: Train 1.000000, Test 0.958500, Error 0.005896\n",
            "Random Forest: Train 0.998257, Test 0.988999, Error 0.005896\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDANiQfx_ppU"
      },
      "source": [
        "rf = RandomForestRegressor(n_estimators = 110, random_state = 1)\n",
        "rf.fit(X_train1, y1_train)\n",
        "y1_pred = rf.predict(X_test1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NqW925aMb7gU"
      },
      "source": [
        "rf.fit(X_train2, y2_train)\n",
        "y2_pred = rf.predict(X_test2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W8vYRbIhQb9X",
        "outputId": "af1578dd-fa81-4f35-fe7d-0a38b2a4ef8d"
      },
      "source": [
        "y1_pred[0] #Compressor"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.0553076680298183"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjc2gJ3PaCeA",
        "outputId": "fc96c2c0-1cbc-4ab8-ddb3-786828a641f1"
      },
      "source": [
        "y1_test[5626] #Compressor"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.6122764910319048"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bExD9JBLJKtd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "139e7c1b-ec15-433c-a215-2e027e371535"
      },
      "source": [
        "data.iloc[5626]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lever_position                          -1.167001\n",
              "Ship_speed                              -1.156500\n",
              "Gas_turbine_shaft_torque                -1.099562\n",
              "Gas_turbine_rate_of_revolutions         -0.991925\n",
              "Gas_generator_rate_of_revolutions       -1.257234\n",
              "Starboard_propeller_torque              -1.000911\n",
              "Port_propeller_torque                   -1.000911\n",
              "HP_turbine_exit_temperature             -1.348581\n",
              "GT_compressor_outlet_air_temperature    -1.194932\n",
              "HP_turbine_exit_pressure                -1.061748\n",
              "GT_compressor_outlet_air_pressure       -1.096239\n",
              "Gas_turbine_exhaust_gas_pressure        -1.004750\n",
              "Turbine_injection_control               -0.945851\n",
              "Fuel_flow                               -1.044248\n",
              "GT_compressor_decay_state_coefficient   -0.612276\n",
              "GT_turbine_decay_state_coefficient       0.338919\n",
              "Name: 5626, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ABBsWBhhcLeK",
        "outputId": "ead3e8ca-2230-461f-ab18-57a192a9b325"
      },
      "source": [
        "y2_pred[0] #Turbine"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4575620063421368"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NKrBfbuZcRFN",
        "outputId": "fe164373-7801-4230-fc54-e42aab6e55b5"
      },
      "source": [
        "y2_test[9445] #Turbine"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.404283765727103"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JH_aq8ar1xZU",
        "outputId": "f1582993-363f-4fb3-fb58-e04ce7ac127e"
      },
      "source": [
        "data.iloc[251]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lever_position                           1.164657\n",
              "Ship_speed                               1.169058\n",
              "Gas_turbine_shaft_torque                 1.082813\n",
              "Gas_turbine_rate_of_revolutions          1.238230\n",
              "Gas_generator_rate_of_revolutions        1.012218\n",
              "Starboard_propeller_torque               1.061996\n",
              "Port_propeller_torque                    1.061996\n",
              "HP_turbine_exit_temperature              1.107111\n",
              "GT_compressor_outlet_air_temperature     1.163627\n",
              "HP_turbine_exit_pressure                 1.161227\n",
              "GT_compressor_outlet_air_pressure        1.212168\n",
              "Gas_turbine_exhaust_gas_pressure         1.216866\n",
              "Turbine_injection_control                1.063718\n",
              "Fuel_flow                                1.079511\n",
              "GT_compressor_decay_state_coefficient    0.745201\n",
              "GT_turbine_decay_state_coefficient      -1.392298\n",
              "Name: 251, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vUV_B_yUYP5y",
        "outputId": "efbfbbb9-1ea0-48a1-f2a8-7ec38f2a28da"
      },
      "source": [
        "y1_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4633   -1.019520\n",
              "2179   -0.001412\n",
              "8508   -1.494637\n",
              "7559    1.152444\n",
              "3386   -0.069286\n",
              "          ...   \n",
              "8479    0.541579\n",
              "2609   -0.544403\n",
              "1827    1.559687\n",
              "9176    0.541579\n",
              "8058   -1.562510\n",
              "Name: GT_compressor_decay_state_coefficient, Length: 3000, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WIsL8-88GziY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2905562c-bdf6-4d19-8240-c59b068c59af"
      },
      "source": [
        "y2_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9580    0.472090\n",
              "4428    0.205749\n",
              "339     1.537454\n",
              "9445    1.404284\n",
              "3930   -0.193763\n",
              "          ...   \n",
              "6568   -0.859616\n",
              "7986    0.738431\n",
              "8666   -1.392298\n",
              "2665   -0.859616\n",
              "5412    1.004772\n",
              "Name: GT_turbine_decay_state_coefficient, Length: 3000, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LrOHgEbzJVx"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}